Search Results (18)

Field-based diagnostic technologies which aid in the early detection of bovine respiratory disease (BRD) are of great need, given the rising attention related to animal welfare and antimicrobial stewardship. This induced BRD study followed 12 Holstein calves through pre-challenge (day 1–3) and post-challenge (day 6–13) periods with daily sampling of nasal secretions with nasal swabs and expired air with air collection bags for determination of BRD status by use of an electronic nose (eNose). Animals were challenged with bovine herpes virus-1 (BHV-1) on day 3 following sample collection and Mannheimia haemolytica on day 5. Results demonstrated a high degree of accuracy for the eNose in correctly classifying pre-challenge samples for nasal swabs (93.5%) and expired air (96.8%). Post-challenge correct classification by the eNose was 97.8% for nasal swabs and 72.5% for expired air samples. Logistical regression was used to determine the probability of agreement between eNose classification and actual animal BRD status by study day. The largest discrepancy between nasal swab and expired air samples fell on days 6 and 7, immediately following the bacterial challenge. The eNose demonstrated potential as a field-based diagnostic tool for the detection of BRD with nasal swabs as the optimal sample type. Full article

Exhaled breath analysis using electronic noses (e-noses) is a promising non-invasive diagnostic tool. However, a lack of standardized protocols limits clinical implementation. This study evaluates the consistency of breathprints in healthy subjects using the Cyranose 320 e-nose to support standardization efforts. Breath samples from 139 healthy non-smoking subjects (age range 18–65 years) were collected using a standardized protocol. Participants exhaled into a Tedlar bag for immediate analysis with the Cyranose 320. Principal Component Analysis (PCA) was used to reduce data dimensionality, and K-means clustering grouped subjects based on breathprints. PCA identified four principal components explaining 97.15% of variance. K-means clustering revealed two clusters: 1 outlier and 138 subjects with highly similar breathprints. The median distance from the cluster center was 0.21 (IQR: 0.18–0.24), indicating low variability. Box plots confirmed breathprint consistency across subjects. The high consistency of breathprints in healthy subjects supports the feasibility of standardizing e-nose protocols. These findings highlight the potential of e-noses for clinical diagnostics, warranting further research in diverse populations and disease cohorts. Full article

The efficient, economical, and sustainable production of biodiesel from waste cooking oils (WCOs) depends on the availability of simple, rapid, and low-cost methods to test the quality of potential feedstocks. The aim of this study was to establish the applicability of stochastic modeling of e-nose profiles in the evaluation of recycled WCO characteristics. Olfactory profiles of 10 WCOs were determined using a Sensigent Cyranose^® 320 chemical vapor-sensing device with a 32 sensor-array, and a stepwise multiple linear regression (MLR) analysis was performed to select stochastic parameters (explanatory variables) for inclusion in the final predictive models of the physicochemical properties of the WCOs. The most important model parameters for the characterization of WCOs were those relating to the time of inception of the e-nose signal “plateau” and to the concentration of volatile organic compounds (VOCs) in the sensor region. A comparison of acid values, peroxide values, water contents, and kinematic viscosities predicted by the MLR models with those determined by conventional laboratory methods revealed that goodness of fit and predictor accuracy varied from good to excellent, with all metric values >90%. Combining e-nose profiling with stochastic modeling was successful in predicting the physicochemical characteristics of WCOs and could be used to select suitable raw materials for efficient and sustainable biodiesel production. Full article

Exhaled breath analysis using an e-nose is a groundbreaking tool for exhaled volatile organic compound (VOC) analysis, which has already shown its applicability in several respiratory and systemic diseases. It is still unclear whether food intake can be considered a confounder when analyzing the VOC-profile. We aimed to assess whether an e-nose can discriminate exhaled breath before and after predefined food intake at different time periods. We enrolled 28 healthy non-smoking adults and collected their exhaled breath as follows: (a) before food intake, (b) within 5 min after food consumption, (c) within 1 h after eating, and (d) within 2 h after eating. Exhaled breath was collected by a formerly validated method and analyzed by an e-nose (Cyranose 320). By principal component analysis, significant variations in the exhaled VOC-profile were shown for principal component 1 (capturing 63.4% of total variance) when comparing baseline vs. 5 min and vs. 1 h after food intake (both p < 0.05). No significance was shown in the comparison between baseline and 2 h after food intake. Therefore, the exhaled VOC-profile seems to be influenced by very recent food intake. Interestingly, two hours might be sufficient to avoid food induced alterations of exhaled VOC-spectrum when sampling for research protocols. Full article

Wood rot fungus Fulvifomes siamensis infects multiple urban tree species commonly planted in Singapore. A commercial e-nose (Cyranose 320) was used to differentiate some plant and fungi volatiles. The e-nose distinctly clustered the volatiles at 0.25 ppm, and this sensitivity was further increased to 0.05 ppm with the use of nitrogen gas to purge the system and set up the baseline. Nitrogen gas baseline resulted in a higher magnitude of sensor responses and a higher number of responsive sensors. The specificity of the e-nose for F. siamensis was demonstrated by distinctive clustering of its pure culture, fruiting bodies collected from different tree species, and in diseased tissues infected by F. siamensis with a 15-min incubation time. This good specificity was supported by the unique volatile profiles revealed by SPME GC-MS analysis, which also identified the signature volatile for F. siamensis—1,2,4,5-tetrachloro-3,6-dimethoxybenzene. In field conditions, the e-nose successfully identified F. siamensis fruiting bodies on different tree species. The findings of concentration-based clustering and host-tree-specific volatile profiles for fruiting bodies provide further insights into the complexity of volatile-based diagnosis that should be taken into consideration for future studies. Full article

Foliar fertilisation is known to influence the physiological response of Humulus lupulus (hop plants), but its effect on the flavour profile of beer still has to be investigated. By comparing the effects of four fertilisation treatments, this study aims at determining whether different foliar fertilisation treatments have a significant impact on hop plants’ aromatic quality and that of the beer produced. Hop cones harvested from each experimental treatment were brewed to obtain five single dry-hopped beers, which were subsequently analysed. Gas chromatography–mass spectrometry (GC-MS) and electronic nose (Cyranose 320) analyses were performed on the hop cones, while headspace solid-phase microextraction–gas chromatography–mass spectrometry HS-SPME-GC-MS, electronic nose and sensory analyses were carried out on the beers produced. The analyses not only allowed for a differentiation between the hops from the four fertilisation treatments and the control but also enabled a differentiation between the beers produced for their identification. Sensory evaluation revealed consumer preferences regarding the dry-hopped beers analysed, evidencing their distinctive features, including significant differences in both aroma and flavour. Full article

Electronic nose (e-nose) is a new technology applied for the identification of volatile organic compounds (VOC) in breath air. Measuring VOC in exhaled breath can adequately identify airway inflammation, especially in asthma. Its noninvasive character makes e-nose an attractive technology applicable in pediatrics. We hypothesized that an electronic nose could discriminate the breath prints of patients with asthma from controls. A cross-sectional study was conducted and included 35 pediatric patients. Eleven cases and seven controls formed the two training models (models A and B). Another nine cases and eight controls formed the external validation group. Exhaled breath samples were analyzed using Cyranose 320, Smith Detections, Pasadena, CA, USA. The discriminative ability of breath prints was investigated by principal component analysis (PCA) and canonical discriminative analysis (CDA). Cross-validation accuracy (CVA) was calculated. For the external validation step, accuracy, sensitivity and specificity were calculated. Duplicate sampling of exhaled breath was obtained for ten patients. E-nose was able to discriminate between the controls and asthmatic patient group with a CVA of 63.63% and an M-distance of 3.13 for model A and a CVA of 90% and an M-distance of 5.55 for model B in the internal validation step. In the second step of external validation, accuracy, sensitivity and specificity were 64%, 77% and 50%, respectively, for model A, and 58%, 66% and 50%, respectively, for model B. Between paired breath sample fingerprints, there were no significant differences. An electronic nose can discriminate pediatric patients with asthma from controls, but the accuracy obtained in the external validation was lower than the CVA obtained in the internal validation step. Full article

Currently, in clinical practice there are still no useful markers available that are able to diagnose renal cancer in the early stages in the context of population screening. This translates into very high costs for healthcare systems around the world. Analysing urine using an electronic nose (EN) provides volatile organic compounds that can be easily used in the diagnosis of urological diseases. Although no convincing results have been published, some previous studies suggest that dogs trained to sniff urine can recognize different types of tumours (bladder, lung, breast cancer) with different success rates. We therefore hypothesized that urinary volatilome profiling may be able to distinguish patients with renal cancer from healthy controls. A total of 252 individuals, 110 renal patients and 142 healthy controls, were enrolled in this pilot monocentric study. For each participant, we collected, stabilized (at 37 °C) and analysed urine samples using a commercially available electronic nose (Cyranose 320^®). Principal component (PCA) analyses, discriminant analysis (CDA) and ROC curves were performed to provide a complete statistical analysis of the sensor responses. The best discriminating principal component groups were identified with univariable ANOVA analysis. The study correctly identified 79/110 patients and 127/142 healthy controls, respectively (specificity 89.4%, sensitivity 71.8%, positive predictive value 84.04%, negative predictive value 80.37%). In order to test the study efficacy, the Cross Validated Accuracy was calculated (CVA 81.7%, p < 0.001). At ROC analysis, the area under the curve was 0.85. The results suggest that urine volatilome profiling by e-Nose seems a promising, accurate and non-invasive diagnostic tool in discriminating patients from controls. The low costs and ease of execution make this test useful in clinical practice. Full article

Sensor drift is a well-known disadvantage of electronic nose (eNose) technology and may affect the accuracy of diagnostic algorithms. Correction for this phenomenon is not routinely performed. The aim of this study was to investigate the influence of eNose sensor drift on the development of a disease-specific algorithm in a real-life cohort of inflammatory bowel disease patients (IBD). In this multi-center cohort, patients undergoing colonoscopy collected a fecal sample prior to bowel lavage. Mucosal disease activity was assessed based on endoscopy. Controls underwent colonoscopy for various reasons and had no endoscopic abnormalities. Fecal eNose profiles were measured using Cyranose 320^®. Fecal samples of 63 IBD patients and 63 controls were measured on four subsequent days. Sensor data displayed associations with date of measurement, which was reproducible across all samples irrespective of disease state, disease activity state, disease localization and diet of participants. Based on logistic regression, corrections for sensor drift improved accuracy to differentiate between IBD patients and controls based on the significant differences of six sensors (p = 0.004; p < 0.001; p = 0.001; p = 0.028; p < 0.001 and p = 0.005) with an accuracy of 0.68. In this clinical study, short-term sensor drift affected fecal eNose profiles more profoundly than clinical features. These outcomes emphasize the importance of sensor drift correction to improve reliability and repeatability, both within and across eNose studies. Full article

The aim of our study was to assess whether a polymer-based e-nose can distinguish head and neck cancer subjects from healthy controls, as well as from patients with allergic rhinitis. A total number of 45 subjects participated in this study. The first group was composed of 15 patients with histology confirmed diagnosis of head and neck cancer. The second group was made up of 15 patients with diagnoses of allergic rhinitis. The control group consisted of 15 subjects with a negative history of upper airways and/or chest symptoms. Exhaled breath was collected from all participants and sampled by a polymer-based e-nose (Cyranose 320, Sensigent, Pasadena, CA, USA). In the Principal Component Analysis plot, patients with head and neck cancer clustered distinctly from the controls as well as from patients with allergic rhinitis. Using canonical discriminant analysis, the three groups were discriminated, with a cross validated accuracy% of 75.1, p < 0.01. The area under the curve of the receiver operating characteristic curve for the discrimination between head and neck cancer patients and the other groups was 0.87. To conclude, e-nose technology has the potential for application in the diagnosis of head and neck cancer, being an easy, quick, non-invasive and cost-effective tool. Full article

Breath analysis using an electronic nose (e-nose) is an innovative tool for exhaled volatile organic compound (VOC) analysis, which has shown potential in several respiratory and systemic diseases. It is still unclear whether cigarette smoking can be considered a confounder when analyzing the VOC-profile. We aimed to assess whether an e-nose can discriminate exhaled breath before and after smoking at different time periods. We enrolled 24 healthy smokers and collected their exhaled breath as follows: (a) before smoking, (b) within 5 min after smoking, (c) within 30 min after smoking, and (d) within 60 min after smoking. Exhaled breath was collected by a previously validated method and analyzed by an e-nose (Cyranose 320). By principal component analysis, significant variations in the exhaled VOC profile were shown for principal component 1 and 2 before and after smoking. Significance was higher 30 and 60 min after smoking than 5 min after (p < 0.01 and <0.05, respectively). Canonical discriminant analysis confirmed the above findings (cross-validated values: baseline vs. 5 min = 64.6%, AUC = 0.833; baseline vs. 30 min = 83.6%, AUC = 0.927; baseline vs. 60 min = 89.6%, AUC = 0.933). Thus, the exhaled VOC profile is influenced by very recent smoking. Interestingly, the effect seems to be more closely linked to post-cigarette inflammation than the tobacco-related odorants. Full article

Electronic devices have been used to describe chemical compounds in the food industry. However, there are different models and manufacturers of these devices; thus, there has been little consistency in the type of compounds and methods used for identification. This work aimed to determine the applicability of electronic nose (e-nose) Cyroanose 320 to describe the differentiation of volatile organic compounds (VOCs) in fresh Mexican cheese (F-MC) formulated with milk from two different dairy cattle breeds. The VOCs were described using a device manufactured by Sensigent and Solid-Phase Micro-extraction (SPME) coupled to GC-MS as a complementary method. The multivariate principal components analysis (PCA) and the partial least squares discriminant analysis (PLS-DA) were used to describe the relationships of VOCs to electronic nose data, sensory data, and response levels. In addition, variable importance in projection (VIP) was performed to characterize the e-nose signals to the VOCs. The e-nose distinguishes F-MC prepared with milk from two dairy breeds. Sensor number 31 correlated with carboxylic acids most in F-MC from Jersey milk. The HS-SPME/GC-MS identified eighteen VOCs in F-MC made with Holstein milk, while only eleven VOCs were identified for F-MC made with Jersey milk. The more significant peaks in both chromatogram analyses were Propanoic acid, 2-methyl-, 1-(1,1-dimethylethyl)-2-methyl-1,3-propanediyl ester in cheese made from Holstein milk and Propanoic acid, 2-methyl-, 3-hydroxy-2,4,4-trimethylpentyl ester in Jersey milk cheese. Both compounds are considered essential carboxylic acids in the dairy industry. Thus, sensor 31 in the electronic nose Cyranose 320 increased its response by essential carboxylic acids identified by HS-SPME/GC-MS as a complementary method. The e-nose Cyranose 320 is potentially helpful for evaluating fresh Mexican cheese authentication independent of cows’ milk samples from different breeds. Full article

E-noses are innovative tools used for exhaled volatile organic compound (VOC) analysis, which have shown their potential in several diseases. Before obtaining a full validation of these instruments in clinical settings, a number of methodological issues still have to be established. We aimed to assess whether variations in breathing rhythm during wash-in with VOC-filtered air before exhaled air collection reflect changes in the exhaled VOC profile when analyzed by an e-nose (Cyranose 320). We enrolled 20 normal subjects and randomly collected their exhaled breath at three different breathing rhythms during wash-in: (a) normal rhythm (respiratory rate (RR) between 12 and 18/min), (b) fast rhythm (RR > 25/min) and (c) slow rhythm (RR < 10/min). Exhaled breath was collected by a previously validated method (Dragonieri et al., J. Bras. Pneumol. 2016) and analyzed by the e-nose. Using principal component analysis (PCA), no significant variations in the exhaled VOC profile were shown among the three breathing rhythms. Subsequent linear discriminant analysis (LDA) confirmed the above findings, with a cross-validated accuracy of 45% (p = ns). We concluded that the exhaled VOC profile, analyzed by an e-nose, is not influenced by variations in breathing rhythm during wash-in. Full article

Fecal volatile organic compounds (VOC) reflect human and gut microbiota metabolic pathways and their interaction. VOC behold potential as non-invasive preclinical diagnostic biomarkers in various diseases, e.g., necrotizing enterocolitis and late onset sepsis. There is a need for standardization and assessment of the influence of clinical and environmental factors on the VOC outcome before this technique can be applied in clinical practice. The aim of this study was to investigate the influence of gestational age (GA) and mode of delivery on the fecal VOC pattern in preterm infants born below 30 weeks of gestation. Longitudinal fecal samples, collected on days 7, 14, and 21 postnatally, were analyzed by an electronic nose device (Cyranose 320^®). In total, 58 preterm infants were included (29 infants born at GA 24–26 weeks vs. 29 at 27–29 completed weeks, 24 vaginally born vs. 34 via C-section). No differences were identified at any predefined time point in terms of GA and delivery mode (p > 0.05). We, therefore, concluded that correction for these factors in this population is not warranted when performing fecal VOC analysis in the first three weeks of life. Full article

Background: Urinary tract infections (UTI) are among the most common infections in children. The primary tool to detect UTI is dipstick urinalysis; however, this has limited sensitivity and specificity. Therefore, urine culture has to be performed to confirm a UTI. Urinary volatile organic compounds (VOC) may serve as potential biomarker for diagnosing UTI. Previous studies on urinary VOCs focused on detection of UTI in a general population; therefore, this proof-of-principle study was set up in a clinical high-risk pediatric population. Methods: This study was performed at a tertiary nephro-urological clinic. Patients included were 0–18 years, clinically suspected of a UTI, and had abnormal urinalysis. Urine samples were divided into four groups, i.e., urine without bacterial growth, contamination, colonization, and UTI. VOC analysis was performed using an electronic nose (eNose) (Cyranose 320^®) and VOC profiles of subgroups were compared. Results: Urinary VOC analysis discriminated between UTI and non-UTI samples (AUC 0.70; p = 0.048; sensitivity 0.67, specificity 0.70). The diagnostic accuracy of VOCs improved when comparing urine without bacterial growth versus with UTI (AUC 0.80; p = 0.009, sensitivity 0.79, specificity 0.75). Conclusions: In an intention-to-diagnose high-risk pediatric population, UTI could be discriminated from non-UTI by VOC profiling, using an eNose. Since eNose can be used as bed-side test, these results suggest that urinary VOC analysis may serve as an adjuvant in the diagnostic work-up of UTI in children. Full article